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2304596

Project Grant

Overview

Grant Description
SBIR Phase I: The Automated Forensic Economist: Towards affordability, transparency, and efficiency in forensic economics - The broader impact/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project capitalizes on the inefficiencies of the existing expert witness industry and brings affordability, versatility (unlimited scenario generation), simplicity, transparency, and standardization to legal proceedings.

The team will develop an ecosystem that will keep experts accountable and credible (using a peer-review system) and allow lawyers and their clients to be better informed with enhanced access to high-quality services. The team has validated that a profitable market exists for a standardized and automated method of evaluating economic losses in civil legal disputes in an automated, fast, inexpensive, standard, and objective manner.

The quality of expert witness services will increase across the industry, as experts will be able to focus more deeply on the more disputed issues of litigation rather than the automatable portions of the estimation process. Better-informed attorneys and firms will be able to counsel their clients and develop better legal strategies throughout the process, while judicial personnel and juries will benefit from improved legal and expert witness services, gaining access to more standardized information to make better, more-informed decisions and be less susceptible to biased or inaccurate opinions.

This SBIR Phase I project consists of a set of deterministic algorithms intaking user inputs (facts and data regarding parties in a lawsuit) and retrieving relevant data series from pre-harmonized external databases, which are then processed through a set of economic and statistical computations, producing a set of outputs, including an estimate of financial gains (losses) for the lawsuit characterized by user inputs.

The proposed innovation improves on the inefficiencies of the existing expert witness industry in several dimensions, and as a result brings affordability, versatility, simplicity, transparency, and standardization. In Phase I, the algorithm and a usable prototype for capturing employment and personal injury-related financial gains (losses) estimation will be developed.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Subawards are not planned for this award.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Awarding / Funding Agency
Place of Performance
Austin, Texas 78702-4585 United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the End Date has been extended from 04/30/24 to 09/30/24 and the total obligations have increased 8% from $257,604 to $277,604.
Industrial Analytics And Modeling was awarded Project Grant 2304596 worth $277,604 from National Science Foundation in September 2023 with work to be completed primarily in Austin Texas United States. The grant has a duration of 1 year and was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.

SBIR Details

Research Type
SBIR Phase I
Title
SBIR Phase I:The Automated Forensic Economist: Towards Affordability, Transparency, and Efficiency in Forensic Economics
Abstract
The broader impact/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project capitalizes on the inefficiencies of the existing expert witness industry and brings affordability, versatility (unlimited scenario generation), simplicity, transparency, and_x000D_ standardization to legal proceedings. The team will develop an ecosystem that will keep experts accountable and credible (using a peer-review system) and allow lawyers and their clients to be better informed with enhanced access to high-quality services. The team has validated that a profitable market exists for a standardized and automated method of evaluating economic losses in civil legal disputes in an automated, fast,_x000D_ inexpensive, standard, and objective manner. The quality of expert witness services will increase across the industry, as experts will be able to focus more deeply on the more disputed issues of litigation rather than the automatable portions of the estimation process. Better-informed attorneys and firms will be able to counsel their clients and develop better legal strategies throughout the process, while judicial personnel and juries will benefit from improved legal and expert witness services, gaining access to more standardized information to make better, more-informed decisions and be less susceptible to biased or inaccurate opinions._x000D_ _x000D_ This SBIR Phase I project consists of a set of deterministic algorithms intaking user inputs (facts and data regarding parties in a lawsuit) and retrieving relevant data series from pre-harmonized external databases, which are then processed through a set of economic and statistical computations, producing a set of outputs, including an estimate of financial gains (losses) for the lawsuit characterized by user inputs. The proposed innovation improves on the inefficiencies of the existing expert witness industry in several dimensions, and as a result brings affordability, versatility, simplicity, transparency, and standardization. In Phase I, the algorithm and a usable prototype for capturing employment and personal injury-related financial gains (losses) estimation will be developed._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AA
Solicitation Number
NSF 22-551

Status
(Complete)

Last Modified 7/23/24

Period of Performance
9/1/23
Start Date
9/30/24
End Date
100% Complete

Funding Split
$277.6K
Federal Obligation
$0.0
Non-Federal Obligation
$277.6K
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to 2304596

Transaction History

Modifications to 2304596

Additional Detail

Award ID FAIN
2304596
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
RHLKUMEAM989
Awardee CAGE
857E4
Performance District
TX-37
Senators
John Cornyn
Ted Cruz

Budget Funding

Federal Account Budget Subfunction Object Class Total Percentage
Research and Related Activities, National Science Foundation (049-0100) General science and basic research Grants, subsidies, and contributions (41.0) $257,604 100%
Modified: 7/23/24